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Effective Load Carrying Capability of Wind Generation: Initial Results with Public Data. Presented by Ed Kahn at UCEI POWER Conference March 18,2005. Background: What are wind integration costs?. Its all about reserves Wind power has an intermittent output profile

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Effective load carrying capability of wind generation initial results with public data l.jpg

Effective Load Carrying Capability of Wind Generation: Initial Resultswith Public Data

Presented by Ed Kahn

at

UCEI POWER Conference

March 18,2005


Background what are wind integration costs l.jpg
Background: What are wind integration costs? Initial Results

  • Its all about reserves

    • Wind power has an intermittent output profile

    • Some kind of back-up requirement is necessary to accommodate fluctuations

  • Extreme cases

    • German grid rules require that wind power look exactly like thermal (next slide)

    • Wind Collaborative study of regulation requirements: there are no back-up costs

  • What is effective load carrying capability ?

    • A measure of the incremental impact of new resources on planning reserves; based on traditional reliability methods, i.e. loss of load probability (LOLP)

    • Related to resource adequacy issues

      • Stoft LICAP testimony for the NE ISO proposes a transformation of LOLP for making capacity payments

    • Distinct from operating reserve issues

  • Transmission costs are yet another integration issue


German grid rules imposes large integration costs from sacharowitz l.jpg
German grid rules imposes large integration costs Initial Results(from Sacharowitz)


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Overview of ELCC study Initial Results

  • Background

    • California Wind Collaborative RPS Integration Study finds the capacity credit for wind generation is roughly equal to its capacity factor (about 25%)

    • SCE asked Analysis Group to review study methods and make an independent assessment

  • Methodology

    • LOLP methods used in the RPS Study aregenerally reasonable, but their implementation is unclear

    • The use of confidential ISO data defeats the purpose of transparency

  • Data issues

    • Public dataon loads, imports and hydro dispatch must be used in the absence of the ISO data available to the RPS study

      Initial results

    • Base case results do not replicate RPS study results

    • ELCC for 2002 is 13% using SCE wind data, not the 22-23.9% found in the RPS study

    • ELCC for 2003 is about 20% and for 1999 about 10%


Methodology l.jpg
Methodology Initial Results

  • Formally, we define the probability that in a given hour available capacity is less than load. We call this the LOLP for hour i, or LOLPi

  • LOLPi = Pr (∑ Cj < Li), (1)

  • where Cj is the random variable representing the capacity of generator j in hour i and Li is the load in hour i.

  • The annual LOLE index is defined over all hours of the year i as

  • LOLE = ∑ LOLPi. (2)

  • Effective load carrying capacity (ELCC) is the amount of new load, call it ∆L, that can be added to a system at the initial LOLE, which we call LOLEI, after a new unit with capacity ∆Cmax is added. If we denote the random variable representing the available capacity of ∆Cmax by ∆C, then solving (3) for ∆L gives an implicit definition of ELCC.

  • LOLEI = ∑ Pr (∑ Cj + ∆C < Li + ∆L) (3)

  • In normalized form

    ELCC = ∆L/∆Cmax (4)


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Data issues Initial Results

  • RPS study relied on ISO data for 2002 that is not publicly available

    • Hourly hydro generation data

    • Proprietary database for outages

      This analysis relies upon public data from ISO released by FERC in connection with the Western Energy Markets Investigation

    • Hourly hydro from 2000 used as a proxy for 2002

    • Adjustments to load required to account for SMUD withdrawal from ISO control area

    • Forced outage rates from Henwood database


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Initial results Initial Results

  • ELCC depends upon coincidence of wind output with high LOLP hours

    • LOLP is highly correlated with load

      • The coincidence of wind generation and summer peak demand is low (see next slide)

    • There is year to year variation in this correlation

    • Correlation was comparatively low in 2002; higher in 2003

      ELCC is also sensitive to the concentration of LOLE in time; i.e. is 95% of LOLE in the top 20 hours or the top 50 hours

    • We use “perfect load shaving” hydro dispatch in base case; this tends to concentrate the LOLE to the top load hours

    • The LOLE can be spread out more by tightening the supply/demand balance and raising the total LOLE

    • RPS results appear to have a bigger LOLE spread than Analysis Group base case (see Figures 3.1 and 3.2 and compare with following chart)

    • It is unclear why the RPS study finds 50 high LOLP hours instead of 20




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Sensitivity cases Initial Results

  • Disaggregated wind data

  • Representation of hydro dispatch

  • Sensitivity to forced outage rates

  • 2003 data

  • 1999 data including adjustments for high LOLE levels




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1999 Data with 2691 MW of Additional Perfect Capacity and 2003 Data

  • ELCC does not depend on LOLE, when LOLE is low, but the 1999 Results show LOLE is high (about 23 hours/year)

  • We add enough capacity to reduce LOLE to the “1 day in ten years” level (about 2.4 hours/year)

  • This reduces ELCC by 40% (from 16.5% to 10%)


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Conclusions and 2003 Data

  • ELCC methods are a reasonable approach to determining the capacity value of wind generation, but

  • Implementing these methods raises issues

  • The RPS Integration relies on confidential data used in a non-transparent way

  • Year to year variation is substantial

  • This raises policy questions about how to compensate wind generators for capacity

  • Other estimates of wind integration costs in the RPS study need further examination